IB Standards

Unit 3 is aligned with the International Baccaulaureate Computer Science syallabus, theme A4 Machine Learning. The goal is to provide students with Constructionist experience with these concepts, going beyond the "describe" command term of IB's syllabus. We are not aiming for complete coverage of the standards.

Concept Subconcept Standard Coverage
A4 Machine Learning Machine learning fundamentals A4.1.1. Describe the types of machine learning and their applications in the real world.
A4 Machine Learning Machine learning fundamentals A4.1.2. Describe the hardware requirements for various scenarios where machine learning is deployed.
A4 Machine Learning Data preprocessing (HL only) A4.2.1. Describe the significance of data cleaning.
A4 Machine Learning Data preprocessing (HL only) A4.2.2. Describe the role of feature selection.
A4 Machine Learning Data preprocessing (HL only) A4.2.3. Describe the importance of dimensionality reduction.
A4 Machine Learning Machine learning approaches (HL only) A4.3.1. Explain how linear regression is used to predict continuous outcomes.
A4 Machine Learning Machine learning approaches (HL only) A4.3.2. Explain how classifications techniques in supervised learning are used to predict discrete categorical outcomes.
A4 Machine Learning Machine learning approaches (HL only) A4.3.3. Explain the role of hyperparameter tuning when evaluating supervised learning algorithms.
A4 Machine Learning Machine learning approaches (HL only) A4.3.4. Describe how clustering techniques in unsupervised learning are used to group data based on similarities in features.
A4 Machine Learning Machine learning approaches (HL only) A4.3.5. Describe how learning techniques using the association rule are used to uncover relations between different attributes in large data sets.
A4 Machine Learning Machine learning approaches (HL only) A4.3.6. Describe how an agent learns to make decisions by interacting with its environment in reinforcement learning.
A4 Machine Learning Machine learning approaches (HL only) A4.3.7. Describe the application of genetic algorithms in various real-world situations.
A4 Machine Learning Machine learning approaches (HL only) A4.3.8. Outline the structure and function of ANNs and how multi-layer networks are used to model complex patterns in data sets.
A4 Machine Learning Machine learning approaches (HL only) A4.3.9. Describe how CNNs are designed to adaptively learn spatial hierarchies of features in images.
A4 Machine Learning Machine learning approaches (HL only) A4.3.10. Explain the importance of model selection and comparison in machine learning.
A4 Machine Learning Ethical considerations A4.4.1. Discuss the ethical implications of machine learning in real-world scenarios.
A4 Machine Learning Ethical considerations A4.4.2. Discuss ethical aspects of the increasing integration of computer technologies into daily life.